Objective:The purpose of this study was to construct a radiomics model based on multi-model MRI for noninvasively predicting the 1p/19q deletion status in high-grade gliomas with methylation-positive MGMT.Methods:106 high-grade glioma patients who underwent surgery and were confirmed by pathological biopsy in the Department of Neurosurgery of the First Affiliated Hos-pital of Xinjiang Medical University from September 2021 to September 2023 were retrospectively ana-lyzed,of which 33 patients with MGMT methylation-positive combined 1p19q co-deletion and 73 pa-tients with MGMT methylation-positive combined 1p19q non-co-deficiency.All patients were randomly divided into a training set and a testing set at a ratio of 7∶3.T,WI,T2 WI,T2-FLAIR,and CE-T1WI sequences were selected to outline the tumor region of interest(ROI)layer by layer along the tumor margin and generate the volume of interest(VOI)to extract the radiomics features.Principal compo-nent analysis(PCA)was applied for dimensionality reduction,and ANOVA method was used for fur-ther feature selecting.And then,four machine learning method including auto-encoder(AE),logistic regression(LR),random forest(RF),and support vector machine(SVM)were respectively used to build the radiomics models for predicting MGMT methylation-positive combined with 1p/19q co-dele-tion status.The diagnostic efficacy of each model was assessed by ROC curve analysis.Results:Among the four radiomics models,the AE radiomics model was the optimal model for predicting the 1p/19q deletion status in high-grade gliomas with positive-methylation MGMT,with AUC of 0.924 and 0.864 in the training set and test set,respectively;and the AUCs of the other three radiomics models(LR,RF,and SVM)were 0.950,1.000 and 0.951 in the training set,and 0.777,0.773 and 0.786 in the test set,respectively.Conclusion:Multi-model MRI radiomics-based model can effectively predict MGMT methylation positivity combined with 1p/19q deletion status in high-grade gliomas.
万方数据
维普
